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DeepISLES: a clinically validated ischemic stroke segmentation model from the ISLES'22 challenge.

Ezequiel de la Rosa^1,2,3, Mauricio Reyes^4,5, Sook-Lei Liew^6,7

¹Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland. ezequieldlrosa@gmail.com.

Nature Communications

|August 9, 2025

Summary

This summary is machine-generated.

DeepISLES, an AI algorithm for ischemic stroke lesion segmentation, significantly improves accuracy and generalizability. This robust tool outperforms existing models and is preferred by neuroradiologists, aiding clinical stroke management.

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Area of Science:

Artificial Intelligence
Medical Imaging
Neurology

Background:

Diffusion-weighted MRI is crucial for ischemic stroke diagnosis but AI algorithm generalizability is limited by image variability.
Developing robust AI for accurate ischemic stroke lesion segmentation is a significant clinical challenge.

Purpose of the Study:

To present DeepISLES, a novel ensemble AI algorithm for robust ischemic stroke lesion detection and segmentation.
To evaluate DeepISLES' performance and generalizability on a large external dataset.

Main Methods:

Developed DeepISLES by ensembling top-performing methods from the 2022 Ischemic Stroke Lesion Segmentation challenge.
Validated the algorithm on a large external dataset (N=1685).
Assessed performance using Dice and F1 scores, clinical biomarker extraction, and comparison with expert annotations.

Main Results:

DeepISLES achieved superior accuracy, outperforming state-of-the-art models by 7.4% in Dice score and 12.6% in F1 score.
The algorithm demonstrated strong generalizability across diverse data.
Neuroradiologists preferred DeepISLES segmentations over manual annotations in a Turing-like test.

Conclusions:

DeepISLES offers a robust and accurate solution for ischemic stroke lesion segmentation, enhancing clinical decision-making.
Biomedical challenges are valuable for developing clinically relevant and generalizable AI tools.
The DeepISLES algorithm is publicly available to advance stroke diagnostics.